Developing a Real-Time Trajectory Tool to Identify Potentially Unsafe Concurrent Opioid and Benzodiazepine Use among Older Adults

项目摘要/摘要 处方类阿片类药物过量死亡在过去20年中显著增加，其中三分之一 涉及同时使用苯二氮卓类药物(BZD)的死亡。尽管临床指南和FDA的黑匣子 警告不要同时使用阿片类药物(OPI)和BZD(以下简称OPI-BZD)， 2002至2014年间，使用OPI-BZD的患者增加了41%。OPI-BZD的使用增加了 服药过量和其他不良后果(如跌倒、骨折、认知功能障碍)，特别是在老年人中 成年人。 使用OPI-BZD的剂量和持续时间因患者特征和合并症情况而异。然而， 关于持续时间和剂量的阈值或与OPI-BZD使用情况最相关的模式，人们知之甚少 在老年人中有服药过量和其他不良后果的风险。先前的研究已经定义了OPI-BZD 使用任意阈值(例如，≥1天重叠供应)，并且仅关注持续时间或剂量 而不是同时使用的持续时间和剂量的组合。应用任意和宽泛的阈值 在没有证据和对所有患者进行验证的情况下，给临床护理的量身定做带来了挑战，并导致 涉及OPI-BZD使用的无效治疗和干预。或者，高级基于组的轨迹 模型(GBTM)可以用来更好地描述OPI-BZD在临床实践中的使用情况。GBTM有能力 为了说明动态用药，确定随时间有类似变化的子组，并同时 检查剂量和持续时间阈值或其他与结果最相关的模式，以便更好地帮助临床 决策。 这项拟议的研究旨在开发一种创新的、实时地预测危险阿片类药物-苯二氮卓类药物 用于有效识别和预测老年人(老年人)亚群的轨迹电子护理工具(PROTECT) ≥50年)，具有独特的和潜在的不安全的使用模式。因为无论是医疗保险还是医疗补助 参与者正在经历更多的慢性疼痛状况、心理健康/药物使用 疾病，以及处方OPI的使用，联邦医疗保险和医疗补助是开发保护的理想环境 工具。使用2013-2016年全国联邦医疗保险索赔和亚利桑那州(AZ)和佛罗里达州(FL)的医疗补助数据，目标1 重点是确定OPI-BZD使用的不同轨迹。我们还将确定与以下各项相关的预测因素 特定的轨迹或模式。在目标2中，我们将确定OPI-BZD使用的不同轨迹或模式 这与两种不同的结果(即服药过量；跌倒和骨折)关系最密切。最后， 我们建议开发一个测试版的实时保护平台，能够前瞻性地和迭代地 通过前瞻性分析来自OPI-BZD的更多最新数据来预测OPI-BZD不安全的使用模式 2017-2019年亚利桑那州和佛罗里达州的医疗补助。我们发现和工具的基础结构可以概括为 医疗保险、其他州的医疗补助计划或具有类似数据结构的其他医疗数据系统。

Project Summary/Abstract Prescription opioid overdose deaths have increased markedly in the past two decades, with a third of these fatalities involving concurrent benzodiazepine (BZD) use. Despite clinical guidelines and FDA black-box warnings cautioning against concurrent opioid (OPI) and BZD use (hereafter OPI-BZD use), the number of patients using OPI-BZD increased by 41% between 2002 and 2014. OPI-BZD use increases the risk of overdose and other adverse outcomes (e.g., falls, fractures, cognitive dysfunction), especially among older adults. The dose and duration of OPI-BZD use varies by patient characteristics and comorbid conditions. However, little is known about the thresholds of duration and dose or patterns of OPI-BZD use profiles most associated with the risk of overdose and other adverse outcomes among older adults. Prior studies have defined OPI-BZD use with arbitrary thresholds (e.g., ≥1 day overlapped supply) and focused only on duration or dose alone rather than combinations of duration and dose of concurrent use. Applying arbitrary and broad thresholds without evidence and validation to all patients imposes challenges in tailoring clinical care, and leads to ineffective therapies and interventions involving OPI-BZD use. Alternatively, advanced group-based trajectory models (GBTMs) can be used to better characterize OPI-BZD use in clinical practice. GBTMs have the ability to account for dynamic medication use, identify subgroups with similar changes over time, and simultaneously examine dose and duration thresholds or other patterns most relevant to outcomes in order to better aid clinical decision-making. The proposed study aims at developing an innovative, real-time “Predicting Risky Opioid-Benzodiazepine Trajectory e-Care Tool (PROTeCT)” for efficiently identifying and predicting subgroups of older adults (aged ≥50 years) with distinct and potentially unsafe patterns of OPI-BZD use. Because both Medicare and Medicaid enrollees are experiencing an increased number of chronic pain conditions, mental health/substance use disorders, and prescription OPI use, Medicare and Medicaid are ideal settings for developing the PROTeCT tool. Using national Medicare claims and Arizona (AZ) and Florida (FL) Medicaid data from 2013-2016, Aim 1 focuses on identifying distinct trajectories of OPI-BZD use. We will also identify predictors associated with specific trajectories or patterns. In Aim 2, we will identify the distinct trajectories or patterns of OPI-BZD use that are the most closely associated with two separate outcomes (i.e., overdoses; falls and fractures). Finally, we propose to develop a beta-version of a real-time PROTeCT platform capable of prospectively and iteratively predicting patients with unsafe patterns of OPI-BZD use by prospectively analyzing more recent data from the 2017-2019 Medicaid in AZ and FL. The infrastructure of our findings and tool may be generalizable to Medicare, Medicaid programs in other states or other healthcare data systems with similar data structures.